Penerapan Metode K-Means dalam Pemetaan Karakteristik Sekolah Sasaran Promosi

Pesatnya perkembangan teknologi berdampak pada bagaimana data dikumpulkan. Tingkat produktivitas data yang tinggi akan menjadi sia-sia apabila tidak diikuti dengan kemampuan mengolah data yang dapat menghasilkan informasi yang membantu perkembangan organisasi. Penelitian ini bertujuan membantu Bagian Promosi UNKRISWINA SUMBA dalam memetakan karakteristik dari sekolah sasaran kemudian memberikan alternatif strategi promosi sebagai masukkan dalam merumuskan bentuk promosi institusi. Data yang digunakan berupa data mahasiswa yang telah mendaftar di UNKRISWINA SUMBA sejak tahun 2016 – 2020. Pengolahan Data menggunakan konsep data mining dengan mengaplikasikan algoritma K-Means. Algoritma K-Means digunakan untuk clustering sekolah sasaran promosi sebanyak 4 klaster. Penentuan klaster dilakukan menggunakan elbow method untuk mengetahui nilai k yang optimal untuk melakukan perhitungan. Berdasarkan hasil pemrosesan menggunakan Algoritma K-Means diketahui bahwa terdapat 4 klaster sekolah sasaran promosi dengan karakteristik atau cirinya masing-masing. Setiap klaster perlu diberikan perlakuan khusus dalam menerapkan strategi promosi. Bentuk promosi lainnya dapat dikembangkan sesuai dengan kebutuhan UNKRISWINA SUMBA dengan memperhatikan kemampuan dari kampus. Oleh karena itu, penelitian ini diharapkan menjadi masukkan bagi manajemen UNKRISWINA SUMBA secara umumnya dan Bagian Promosi pada khususnya untuk mendukung kegiatan promosi serta menentukan strategi kerjasama dengan sekolah sasaran promosi.Technology development has an impact on how it is submitted. Data high scale productivity will be useless if it isnt followed by data processing capability that will produce some information to the organization development. This study aimed to help the promotion division of unkriswina sumba in mapping the school targets which later could give the promotion strategy alternatives as an input in formulating institution promotion form. Data used was the students class of 2016-2020 in unkriswina sumba. Data analysis used was mining data approach with a K-Means algorythmn aplication. The K-Means algorithm is used for clustering the promotion target schools as many as 4 clusters. Cluster was decided using elbow method to know the K value to get the optimal/effective measurement. Based on the results of processing using the K-Means Algorithm, it is known that there are 4 clusters of promotion target schools with their respective characteristics. Each cluster needs to be given special treatment in implementing the promotion strategy. Other forms of promotion can be developed according to the needs of UNKRISWINA SUMBA by taking into account the capabilities of the campus. Therefore, this research is expected to be input for the management of UNKRISWINA SUMBA in general and the Promotion Section in particular to support promotional activities and determine cooperation strategies with promotional target schools

Analisis Resiko Longsor berbasis Citra Landsat-8 menggunakan Interpolasi Spasial

Sebagai Negara kepulauan, Indonesia sering menghadapi bencana yang diakibatkan oleh terjadinya perubahan iklim dan cuaca, atau sering disebut bencana hidrometeorologi. Kondisi dan keadan alam yang memungkinkan terjadi bencana disebut sebagai potensi bencana. Cuaca ekstrim dengan curah hujan yang tinggi memungkinkan terjadinya longsor pada kondisi tanah labil serta kemiringan lereng yang curam. Resiko yang diakibatkan longsor dapat berupa resiko ekonomi maupun resiko sosial. Resiko yang ditimbulkan oleh bencana merupakan potensi kerugian pada suatu kawasan dan kurun waktu tertentu. Bentuk resiko bencana dapa berupa gangguan kegiatan masyarakat, hilangnya rasa aman, masyarakat mengungsi, kerusakan atau kehilangan harta, jiwa terancam, luka, sakit dan kematian. Kabupaten Banjarnegara sebagai salah satu daerah yang memiliki potensi resiko tanah longsor. Berdasarkan karakteristik tersebut maka perlu dilakukan analisis resiko tanah longsor untuk memberikan informasi resiko tanah longsor. Pada Penelitian ini dilakukan analisis resiko longsor berbasis Citra Landsat-8 menggunakan metode Interpolasi Spasial. Data curah hujan yang digunakan adalah Data Curah Hujan BMKG Kabupaten Banjarnegara Tahun 2015. Hasil dari penelitian ini menunjukkan bahwa Terdapat 7 Kecamatan yang memiliki tingkat resiko tanah longsor yang sangat tinggi yaitu Susukan, Purworejo Klampok, Mandiraja, Purwonegoro, Bawang dan Wanadadi

Quality Assessment of Photogrammetric Methods—A Workflow for Reproducible UAS Orthomosaics

Unmanned aerial systems (UAS) are cost-effective, flexible and offer a wide range of applications. If equipped with optical sensors, orthophotos with very high spatial resolution can be retrieved using photogrammetric processing. The use of these images in multi-temporal analysis and the combination with spatial data imposes high demands on their spatial accuracy. This georeferencing accuracy of UAS orthomosaics is generally expressed as the checkpoint error. However, the checkpoint error alone gives no information about the reproducibility of the photogrammetrical compilation of orthomosaics. This study optimizes the geolocation of UAS orthomosaics time series and evaluates their reproducibility. A correlation analysis of repeatedly computed orthomosaics with identical parameters revealed a reproducibility of 99% in a grassland and 75% in a forest area. Between time steps, the corresponding positional errors of digitized objects lie between 0.07 m in the grassland and 0.3 m in the forest canopy. The novel methods were integrated into a processing workflow to enhance the traceability and increase the quality of UAS remote sensing.This research was funded by the Hessian State Ministry for Higher Education, Research and the Arts, Germany, as part of the LOEWE priority project Nature 4.0—Sensing Biodiversity. The grassland study was funded by the Spanish Science Foundation FECYT-MINECO through the BIOGEI (GL2013- 49142-C2-1-R) and IMAGINE (CGL2017-85490-R) projects, and by the University of Lleida; and supported by a FI Fellowship to C.M.R. (2019 FI_B 01167) by the Catalan Government

Exchanging screen for non-screen sitting time or physical activity might attenuate depression and anxiety: A cross-sectional isotemporal analysis during early pandemics in South America

Objectives: To examine the theoretical substitutions of screen exposure, non-screen sitting time, moderate and vigorous physical activity with depressive and anxiety symptoms in South American adults during the COVID-19 pandemic. Design: A cross-sectional study during the first months of the COVID-19 pandemic with data from 1981 adults from Chile, Argentina, and Brazil. Methods: Depressive and anxiety symptoms were assessed using the Beck Depression and Anxiety Inventories. Participants also reported physical activity, sitting time, screen exposure, sociodemographic, and tobacco use data. Isotemporal substitution models were created using multivariable linear regression methods. Results: Vigorous physical activity, moderate physical activity, and screen exposure were independently associated with depression and anxiety symptoms. In adjusted isotemporal substitution models, replacing 10 min/day of either screen exposure or non-screen sitting time with any intensity of physical activity was associated with lower levels of depressive symptoms. Improvements in anxiety symptoms were found when reallocating either screen exposure or non-screen sitting time to moderate physical activity. Furthermore, replacing 10 min/day of screen exposure with non-screen sitting time was beneficially associated with anxiety (B = − 0.033; 95 % CI = − 0.059, − 0.006) and depression (B = − 0.026; 95 % CI = − 0.050, − 0.002). Conclusions: Replacement of screen exposure with any intensity of physical activity or non-screen sitting time could improve mental health symptoms. Strategies aiming to reduce depressive and anxiety symptoms highlight physical activity promotion. However, future interventions should explore specific sedentary behaviors as some will relate positively while others negatively.Fil: Sadarangani, Kabir P.. Universidad Autónoma de Chile; Chile. Universidad Diego Portales; ChileFil: Schuch, Felipe Barreto. Universidade Federal de Santa Maria; Brasil. Universidad Autónoma de Chile; ChileFil: de Roia, Gabriela Fernanda. Universidad de Flores. Laboratorio de Estudios en Actividad Física;Fil: Martínez Gomez, David. Universidad Autónoma de Madrid; España. Consejo Superior de Investigaciones Científicas; España. Consortium for Biomedical Research in Epidemiology and Public Health; EspañaFil: Chávez, Róbinson. Universidad Andrés Bello; ChileFil: Lobo, Pablo Roberto. Universidad de Flores. Laboratorio de Estudios en Actividad Física;Fil: Cristi Montero, Carlos. Pontificia Universidad Católica de Valparaíso; ChileFil: Werneck, André O.. Universidade de Sao Paulo; BrasilFil: Alzahrani, Hosam. Taif University; Arabia SauditaFil: Ferrari, Gerson. Universidad de Santiago de Chile; ChileFil: Ibañez, Agustin Mariano. Universidad de San Andrés; Argentina. University of California; Estados Unidos. Trinity College Dublin; Irlanda. Universidad Adolfo Ibañez; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Silva, Danilo R.. Universidade Federal de Sergipe; Brasil. Universidad Pablo de Olavide; EspañaFil: Von Oetinger, Astrid. Universidad Diego Portales; Chile. Universidad Mayor; ChileFil: Matias, Thiago S.. Universidade Federal de Santa Catarina; BrasilFil: Grabovac, Igor. Universidad de Viena; AustriaFil: Meyer, Jacob. Iowa State University; Estados Unido

Is diet partly responsible for differences in COVID-19 death rates between and within countries?

Correction: Volume: 10 Issue: 1 Article Number: 44 DOI: 10.1186/s13601-020-00351-w Published: OCT 26 2020Reported COVID-19 deaths in Germany are relatively low as compared to many European countries. Among the several explanations proposed, an early and large testing of the population was put forward. Most current debates on COVID-19 focus on the differences among countries, but little attention has been given to regional differences and diet. The low-death rate European countries (e.g. Austria, Baltic States, Czech Republic, Finland, Norway, Poland, Slovakia) have used different quarantine and/or confinement times and methods and none have performed as many early tests as Germany. Among other factors that may be significant are the dietary habits. It seems that some foods largely used in these countries may reduce angiotensin-converting enzyme activity or are anti-oxidants. Among the many possible areas of research, it might be important to understand diet and angiotensin-converting enzyme-2 (ACE2) levels in populations with different COVID-19 death rates since dietary interventions may be of great benefit.Peer reviewe

Cabbage and fermented vegetables : From death rate heterogeneity in countries to candidates for mitigation strategies of severe COVID-19

Large differences in COVID-19 death rates exist between countries and between regions of the same country. Some very low death rate countries such as Eastern Asia, Central Europe, or the Balkans have a common feature of eating large quantities of fermented foods. Although biases exist when examining ecological studies, fermented vegetables or cabbage have been associated with low death rates in European countries. SARS-CoV-2 binds to its receptor, the angiotensin-converting enzyme 2 (ACE2). As a result of SARS-CoV-2 binding, ACE2 downregulation enhances the angiotensin II receptor type 1 (AT(1)R) axis associated with oxidative stress. This leads to insulin resistance as well as lung and endothelial damage, two severe outcomes of COVID-19. The nuclear factor (erythroid-derived 2)-like 2 (Nrf2) is the most potent antioxidant in humans and can block in particular the AT(1)R axis. Cabbage contains precursors of sulforaphane, the most active natural activator of Nrf2. Fermented vegetables contain many lactobacilli, which are also potent Nrf2 activators. Three examples are: kimchi in Korea, westernized foods, and the slum paradox. It is proposed that fermented cabbage is a proof-of-concept of dietary manipulations that may enhance Nrf2-associated antioxidant effects, helpful in mitigating COVID-19 severity.Peer reviewe

Nrf2-interacting nutrients and COVID-19 : time for research to develop adaptation strategies

There are large between- and within-country variations in COVID-19 death rates. Some very low death rate settings such as Eastern Asia, Central Europe, the Balkans and Africa have a common feature of eating large quantities of fermented foods whose intake is associated with the activation of the Nrf2 (Nuclear factor (erythroid-derived 2)-like 2) anti-oxidant transcription factor. There are many Nrf2-interacting nutrients (berberine, curcumin, epigallocatechin gallate, genistein, quercetin, resveratrol, sulforaphane) that all act similarly to reduce insulin resistance, endothelial damage, lung injury and cytokine storm. They also act on the same mechanisms (mTOR: Mammalian target of rapamycin, PPAR gamma:Peroxisome proliferator-activated receptor, NF kappa B: Nuclear factor kappa B, ERK: Extracellular signal-regulated kinases and eIF2 alpha:Elongation initiation factor 2 alpha). They may as a result be important in mitigating the severity of COVID-19, acting through the endoplasmic reticulum stress or ACE-Angiotensin-II-AT(1)R axis (AT(1)R) pathway. Many Nrf2-interacting nutrients are also interacting with TRPA1 and/or TRPV1. Interestingly, geographical areas with very low COVID-19 mortality are those with the lowest prevalence of obesity (Sub-Saharan Africa and Asia). It is tempting to propose that Nrf2-interacting foods and nutrients can re-balance insulin resistance and have a significant effect on COVID-19 severity. It is therefore possible that the intake of these foods may restore an optimal natural balance for the Nrf2 pathway and may be of interest in the mitigation of COVID-19 severity

The impact of the heat wave of summer 2003 in SW Europe as observed from satellite imagery

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Optimizing Drone-Based Surface Models for Prescribed Fire Monitoring

Prescribed burning and pyric herbivory play pivotal roles in mitigating wildfire risks, underscoring the imperative of consistent biomass monitoring for assessing fuel load reductions. Drone-derived surface models promise uninterrupted biomass surveillance but require complex photogrammetric processing. In a Mediterranean mountain shrubland burning experiment, we refined a Structure from Motion (SfM) and Multi-View Stereopsis (MVS) workflow to diminish biases in 3D modeling and RGB drone imagery-based surface reconstructions. Given the multitude of SfM-MVS processing alternatives, stringent quality oversight becomes paramount. We executed the following steps: (i) calculated Root Mean Square Error (RMSE) between Global Navigation Satellite System (GNSS) checkpoints to assess SfM sparse cloud optimization during georeferencing; (ii) evaluated elevation accuracy by comparing the Mean Absolute Error (MAE) of six surface and thirty terrain clouds against GNSS readings and known box dimensions; and (iii) complemented a dense cloud quality assessment with density metrics. Balancing overall accuracy and density, we selected surface and terrain cloud versions for high-resolution (2 cm pixel size) and accurate (DSM, MAE = 57 mm; DTM, MAE = 48 mm) Digital Elevation Model (DEM) generation. These DEMs, along with exceptional height and volume models (height, MAE = 12 mm; volume, MAE = 909.20 cm3) segmented by reference box true surface area, substantially contribute to burn impact assessment and vegetation monitoring in fire management systems